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At least 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Constraining Arctic Climate Projections of Wintertime Warming With Surface Turbulent Flux Observations and Representation of Surface-Atmosphere Coupling

The drivers of rapid Arctic climate change—record sea ice loss, warming SSTs, and a lengthening of the sea ice melt season—compel us to understand how this complex system operates and use this knowledge to enhance Arctic predictability. Changing energy flows sparked by sea ice decline, spotlight atmosphere-surface coupling processes as central to Arctic system function and its climate change response. Despite this, the representation of surface turbulent flux parameterizations in models has not kept pace with our understanding. The large uncertainty in Arctic climate change projections, the central role of atmosphere-surface coupling, and the large discrepancy in model representation of surface turbulent fluxes indicates that these processes may serve as useful observational constraints on projected Arctic climate change. This possibility requires an evaluation of surface turbulent fluxes and their sensitivity to controlling factors (surface-air temperature and moisture differences, sea ice, and winds) within contemporary climate models (here Coupled Model Intercomparison Project 6). The influence of individual controlling factors and their interactions is diagnosed using a multi-linear regression approach. This evaluation is done for four sea ice loss regimes, determined from observational sea ice loss trends, to control for the confounding effects of natural variability between models and observations. The comparisons between satellite- and model-derived surface turbulent fluxes illustrate that while models capture the general sensitivity of surface turbulent fluxes to declining sea ice and to surface-air gradients of temperature and moisture, substantial mean state biases exist. Specifically, the central Arctic is too weak of a heat sink to the winter atmosphere compared to observations, with implications to the simulated atmospheric circulation variability and thermodynamic profiles. Models were found to be about 50% more efficient at turning an air-sea temperature gradient anomaly into a sensible heat flux anomaly relative to observations. Further, the influence of sea ice concentration on the sensible heat flux is underestimated in models compared to observations. The opposite is found for the latent heat flux variability in models; where the latent heat flux is too sensitive to a sea ice concentration anomaly. Lastly, the results suggest that present-day trends in sea ice retreat regions may serve as suitable observational constraints of projected Arctic warming.

turbulent fluxes↗
DOE OSTI2023

Rapid discovery of high hardness multi-principal-element alloys using a generative adversarial network model

Multi-principal element alloys (MPEAs) continue to gain research prominence due to their promising high-temperature microstructural and mechanical properties. Recently, machine learning (ML) and materials informatics have been used extensively for screening MPEAs, however, most of these efforts were focused on constructing classification and regression models for predicting phase stability and mechanical properties of known compositions. These approaches may accelerate the screening process but optimizing new compositions with desirable properties within a practical time frame from an infinitely large design space of MPEA systems remains a grand challenge. To tackle this composition optimization challenge, a generative adversarial network coupled with a neural-network ML model was utilized to design MPEAs by filtering compositions that have high hardness. Even in a high-dimensional space with 18 elements as descriptors, the ML model was able to generate optimized compositions from which one composition was found to have 10% higher hardness (941 HV) than the maximum in the training data (857 HV). Density-functional theory was used to provide thermodynamic and electronic insights to higher hardness of the new MPEA found. The present work can optimize compositions from a wide design space of 18 elements (including W, Ta and Nb) that presents an opportunity to synthesize new compositions for applications ranging from corrosion-resistant alloys to nuclear materials. Here the findings suggest that generative ML can greatly accelerate materials discovery by identifying novel compositions, which can serve as a data-informed tool to guide experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Gestational urinary bisphenol A and maternal and newborn thyroid hormone concentrations: The HOME Study

Bisphenol A (BPA), an endocrine disruptor used in consumer products, may perturb thyroid function. Prenatal BPA exposure may have sex-specific effects on thyroid hormones (THs). Our objectives were to investigate whether maternal urinary BPA concentrations during pregnancy were associated with THs in maternal or cord serum, and whether these associations differed by newborn sex or maternal iodine status. We measured urinary BPA concentrations at 16 and 26 weeks gestation among pregnant women in the HOME Study (2003–2006, Cincinnati, Ohio). Thyroid stimulating hormone (TSH) and free and total thyroxine (T{sub 4}) and triiodothyronine (T{sub 3}) were measured in maternal serum at 16 weeks (n=181) and cord serum at delivery (n=249). Associations between BPA concentrations and maternal or cord serum TH levels were estimated by multivariable linear regression. Mean maternal urinary BPA was not associated with cord THs in all newborns, but a 10-fold increase in mean BPA was associated with lower cord TSH in girls (percent change=−36.0%; 95% confidence interval (CI): −58.4, −1.7%), but not boys (7.8%; 95% CI: −28.5, 62.7%; p-for-effect modification=0.09). We observed no significant associations between 16-week BPA and THs in maternal or cord serum, but 26-week maternal BPA was inversely associated with TSH in girls (−42.9%; 95% CI: −59.9, −18.5%), but not boys (7.6%; 95% CI: −17.3, 40.2%; p-for-effect modification=0.005) at birth. The inverse BPA–TSH relation among girls was stronger, but less precise, among iodine deficient versus sufficient mothers. Prenatal BPA exposure may reduce TSH among newborn girls, particularly when exposure occurs later in gestation. - Highlights: • Examined associations of BPA with thyroid hormones in pregnant women and newborns. • Assessed effect modification of BPA–thyroid hormone associations by newborn sex. • Greater BPA related to decreased thyroid stimulating hormone in girls' cord serum. • Results may suggest window of susceptibility to BPA in later gestation. • BPA potentially has greatest adverse effect on girls with iodine deficient mothers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Semi-supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost

Multireference (MR) diagnostics are common tools for identifying strongly correlated electronic structure that makes single-reference (SR) methods (e.g., density functional theory or DFT) insufficient for accurate property prediction. However, MR diagnostics typically require computationally demanding correlated wave function theory (WFT) calculations, and diagnostics often disagree or fail to predict MR effects on properties. To overcome these challenges, we introduce a semi-supervised machine learning (ML) approach with virtual adversarial training (VAT) of an MR classifier using 15 WFT and DFT MR diagnostics as inputs. In semi-supervised learning, only the most extreme SR or MR points are labeled, and the remaining point labels are learned. The resulting VAT model outperforms the alternatives, as quantified by the distinct property distributions of SR- and MR-classified molecules. Additionally, to reduce the cost of generating inputs to the VAT model, we leverage the VAT model’s robustness to noisy inputs by replacing WFT MR diagnostics with regression predictions in an MR decision engine workflow that preserves excellent performance. We demonstrate the transferability of our approach to larger molecules and those with distinct chemical composition from the training set. This MR decision engine demonstrates promise as a low-cost, high-accuracy approach to the automatic detection of strong correlation for predictive high-throughput screening.

36 MATERIALS SCIENCE↗
DOE OSTI2015

[O iii] λ5007 and X-ray properties of a complete sample of hard X-RAY selected AGNs in the local universe

We study the correlation between the [O iii] λ5007 and X-ray luminosities of local active galactic nuclei (AGNs), using a complete, hard X-ray (>10 keV) selected sample in the Swift/BAT 9-month catalog. From our optical spectroscopic observations at the South African Astronomical Observatory and the literature, a catalog of [O iii] λ5007 line flux for all 103 AGNs at Galactic latitudes of |b|>15{sup ∘} is compiled. Significant correlations with intrinsic X-ray luminosity (L{sub X}) are found for both observed (L{sub [O III] }) and extinction-corrected (L{sub [O III]}{sup cor}) luminosities, separately for X-ray unabsorbed and absorbed AGNs. We obtain the regression form of L{sub [O III] }∝ L{sub 2−10 keV}{sup 1.18±0.07} and L{sub [O III]}{sup cor} ∝ L{sub 2−10 keV}{sup 1.16±0.09} from the whole sample. The absorbed AGNs with low (<0.5%) scattering fractions in soft X-rays show on average smaller L{sub [O III]}/L{sub X} and L{sub [O III]}{sup cor}/L{sub X} ratios than the other absorbed AGNs, while those in edge-on host galaxies do not. These results suggest that a significant fraction of this population is buried in tori with small opening angles. By using these L{sub [O III] }versus L{sub X} correlations, the X-ray luminosity function (LF) of local AGNs (including Compton-thick AGNs) in a standard population synthesis model gives much better agreement with the [O iii] λ5007 LF derived from the Sloan Digital Sky Survey than previously reported. This confirms that hard X-ray observations are a very powerful tool to find AGNs with high completeness.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

TU-CD-BRB-05: Radiation Damage Signature of White Matter Fiber Bundles Using Diffusion Tensor Imaging (DTI)

Purpose: To develop an automated and scalable approach and identify temporal, spatial and dosimetric patterns of radiation damage of white matter (WM) fibers following partial brain irradiation. Methods: An automated and scalable approach was developed to extract DTI features of 22 major WM fibers from 33 patients with low-grade/benign tumors treated by radiation therapy (RT). DTI scans of the patients were performed pre-RT, 3- and 6-week during RT, and 1, 6 and 18 months after RT. The automated tractography analysis was applied to 198 datasets as: (1) intra-subject registration of longitudinal DTI, (2) spatial normalization of individual-patient DTI to the Johns Hopkins WM Atlas, (3) automatic fiber tracking regulated by the WM Atlas, and (4) segmentation of WM into 22 major tract profiles. Longitudinal percentage changes in fractional anisotropy (FA), and mean, axial and radial diffusivity (MD/AD/RD) of each tract from pre-RT were quantified and correlated to 95%, 90% and 80% percentiles of doses and mean doses received by the tract. Heatmaps were used to identify clusters of significant correlation and reveal temporal, spatial and dosimetric signatures of WM damage. A multivariate linear regression was further carried out to determine influence of clinical factors. Results: Of 22 tracts, AD/MD changes in 12 tracts had significant correlation with doses, especially at 6 and 18 months post-RT, indicating progressive radiation damage after RT. Most interestingly, the DTI-index changes in the elongated tracts were associated with received maximum doses, suggesting a serial-structure behavior; while short association fibers were affected by mean doses, indicating a parallel-structure response. Conclusion: Using an automated DTI-tractography analysis of whole brain WM fibers, we reveal complex radiation damage patterns of WM fibers. Damage in WM fibers that play an important role in the neural network could be associated with late neurocognitive function declines after brain irradiation. NIH NS064973.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2000

Validity of VO(2 max) in predicting blood volume: implications for the effect of fitness on aging

A multiple regression model was constructed to investigate the premise that blood volume (BV) could be predicted using several anthropometric variables, age, and maximal oxygen uptake (VO(2 max)). To test this hypothesis, age, calculated body surface area (height/weight composite), percent body fat (hydrostatic weight), and VO(2 max) were regressed on to BV using data obtained from 66 normal healthy men. Results from the evaluation of the full model indicated that the most parsimonious result was obtained when age and VO(2 max) were regressed on BV expressed per kilogram body weight. The full model accounted for 52% of the total variance in BV per kilogram body weight. Both age and VO(2 max) were related to BV in the positive direction. Percent body fat contributed <1% to the explained variance in BV when expressed in absolute BV (ml) or as BV per kilogram body weight. When the model was cross validated on 41 new subjects and BV per kilogram body weight was reexpressed as raw BV, the results indicated that the statistical model would be stable under cross validation (e.g., predictive applications) with an accuracy of +/- 1,200 ml at 95% confidence. Our results support the hypothesis that BV is an increasing function of aerobic fitness and to a lesser extent the age of the subject. The results may have implication as to a mechanism by which aerobic fitness and activity may be protective against reduced BV associated with aging.

NASA Discipline Cardiopulmonary↗
NASA NTRS1999

A Synthesis of Solar Cycle Prediction Techniques

A number of techniques currently in use for predicting solar activity on a solar cycle timescale are tested with historical data. Some techniques, e.g., regression and curve fitting, work well as solar activity approaches maximum and provide a month-by-month description of future activity, while others, e.g., geomagnetic precursors, work well near solar minimum but only provide an estimate of the amplitude of the cycle. A synthesis of different techniques is shown to provide a more accurate and useful forecast of solar cycle activity levels. A combination of two uncorrelated geomagnetic precursor techniques provides a more accurate prediction for the amplitude of a solar activity cycle at a time well before activity minimum. This combined precursor method gives a smoothed sunspot number maximum of 154 plus or minus 21 at the 95% level of confidence for the next cycle maximum. A mathematical function dependent on the time of cycle initiation and the cycle amplitude is used to describe the level of solar activity month by month for the next cycle. As the time of cycle maximum approaches a better estimate of the cycle activity is obtained by including the fit between previous activity levels and this function. This Combined Solar Cycle Activity Forecast gives, as of January 1999, a smoothed sunspot maximum of 146 plus or minus 20 at the 95% level of confidence for the next cycle maximum.

Hathaway, David H.↗
NASA NTRS1998

Contrasting actions of pressor agents in severe autonomic failure

BACKGROUND: Orthostatic hypotension is the most disabling symptom of autonomic failure. The choice of a pressor agent is largely empiric, and it would be of great value to define predictors of a response. PATIENTS AND METHODS: In 35 patients with severe orthostatic hypotension due to multiple system atrophy or pure autonomic failure, we determined the effect on seated systolic blood pressure (SBP) of placebo, phenylpropanolamine (12.5 mg and 25 mg), yohimbine (5.4 mg), indomethacin (50 mg), ibuprofen (600 mg), caffeine (250 mg), and methylphenidate (5 mg). In a subgroup of patients, we compared the pressor effect of midodrine (5 mg) with the effect of phenylpropanolamine (12.5 mg). RESULTS: There were no significant differences in the pressor responses between patients with multiple system atrophy or pure autonomic failure. When compared with placebo, the pressor response was significant for phenylpropanolamine, yohimbine, and indomethacin. In a subgroup of patients, we confirmed that this pressor effect of phenylpropanolamine, yohimbine, and indomethacin corresponded to a significant increase in standing SBP. The pressor responses to ibuprofen, caffeine, and methylphenidate were not significantly different from placebo. Phenylpropanolamine and midodrine elicited similar pressor responses. There were no significant associations between drug response and autonomic function testing, postprandial hypotension, or plasma catecholamine levels. CONCLUSIONS: We conclude that significant increases in systolic blood pressure can be obtained in patients with orthostatic hypotension due to primary autonomic failure with phenylpropanolamine in low doses or yohimbine or indomethacin in moderate doses. The response to a pressor agent cannot be predicted by autonomic function testing or plasma catecholamines. Therefore, empiric testing with a sequence of medications, based on the risk of side effects in the individual patient and the probability of a response, is a useful approach.

Clinical Trial↗
DOE OSTI2026

Parametric reduced order models for graded lattice structures

Graded lattice structures, characterized by smoothly varying mechanical properties, hold significant promise for optimizing material distribution in advanced engineering applications. However, accurately modeling these structures poses substantial computational challenges due to the continuous geometric variations within their unit cells. Here, to address these challenges, this paper introduces a novel Efficient Reduced Order Model (EROM) that integrates the Matrix Discrete Empirical Interpolation Method (MDEIM) and Discrete Empirical Interpolation Method (DEIM) with polynomial regression to efficiently manage geometric parametrization in lattice structures. Unlike traditional reduced order models (ROMs) that require extensive precomputed libraries for each geometric configuration, our approach enables continuous geometric variations through a flexible algebraic formulation, significantly reducing computational costs while preserving high accuracy. The method constructs projection matrices for individual unit cells that can be efficiently assembled into global systems, leveraging the repetitive nature of lattice structures. Numerical studies demonstrate that our EROM achieves displacement errors below 1% and von Mises stress prediction errors below 4%, coupled with computational speedups exceeding two orders of magnitude compared to full-order simulations. The proposed method's modularity and scalability make it particularly suitable for design optimization and real-time simulation of functionally graded lattice structures, with applications spanning aerospace to biomedical engineering.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2016

TH-AB-207A-11: Anatomical and Functional Assessment of Coronary Artery Disease Using Low-Dose Whole-Organ Dynamic Computed Tomography

Purpose: To evaluate the accuracy of a low-dose, first-pass-analysis (FPA) dynamic computed tomography angiography and perfusion (CTAP) technique, for whole-organ anatomical and functional assessment of coronary artery disease (CAD). Methods: An angioplasty balloon was advanced into the left anterior descending (LAD) coronary artery of five swine (35–45 kg) to induce several levels of stenosis at maximal hyperemia (intracoronary adenosine, 240 µg/min). Reference fluorescence microspheres and intravenous contrast (370 mg/mL iodine, 25 mL, 7 mL/s) were injected centrally and dynamic imaging was performed using a 320-slice CT scanner at 100 kVp and 200 mA. Twenty volume scans were acquired per stenosis level to capture complete aortic and myocardial enhancement curves, but only two volume scans were used for whole-organ dynamic FPA CTAP measurement. All CTAP measurements in the LAD were compared to the reference microsphere perfusion measurements using linear regression, concordance correlation, and Bland-Altman analysis. Results: The result of dynamic FPA CTAP measurement in the LAD was in good agreement with the reference microsphere perfusion measurement (P-CTAP = 1.01 P-MICRO + 0.16, R{sup 2} = 0.95). The root mean square error (RMSE) and difference (RMSD) of measurement were 0.51 mL/min/g and 0.47 mL/min/g, respectively. Bland-Altman analysis demonstrated negligible systematic measurement bias. Additionally, the concordance correlation coefficient (CCC) was found to be ρ = 0.97, indicating excellent agreement between dynamic FPA CTAP measurement and the reference microsphere perfusion measurement. Lastly, the effective dose of the proposed technique using the “simulated” two-volume scan CTAP acquisition protocol was 2.6 mSv; much lower than the ∼10 mSv effective dose of current dynamic CTP techniques alone. Conclusion: The results indicate the potential for significant improvements in CAD assessment through low-dose, quantitative dynamic FPA CTAP. Such improvements are afforded through whole-organ CT scanning technology, and have the potential to improve patient outcomes and lead to healthier patient lives. Conflict of Interest (only if applicable): Grant funding from Toshiba America Medical Systems.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2018

MODIS Collection 6 MAIAC Algorithm

This paper describes the latest version of the algorithm MAIAC (Multi-Angle Implementation of Atmospheric Correction) used for processing the MODIS (Moderate-resolution Imaging Spectroradiometer) Collection6 data record. Since initial publication in 2011-2012, MAIAC has changed considerably to adapt to global processing and improve cloud/snow detection, aerosol retrievals and atmospheric correction of MODIS data. The main changes include (1) transition from a 25 to 1 km scale for retrieval of the spectral regression coefficient (SRC) which helped to remove occasional blockiness at 25 km scale in the aerosol optical depth (AOD) and in the surface reflectance, (2) continuous improvements of cloud detection, (3) introduction of smoke and dust tests to discriminate absorbing fine- and coarse mode aerosols, (4) adding over-water processing, (5) general optimization of the LUT (LookUp-Table)-based radiative transfer for the global processing, and others. MAIAC provides an interdisciplinary suite of atmospheric and land products, including cloud mask (CM), column water vapor (CWV), AOD at 0.47 and 0.55 m, aerosol type (background, smoke or dust) and fine-mode fraction over water; spectral bidirectional reflectance factors (BRF), parameters of Ross-thick Lisparse (RTLS) bidirectional reflectance distribution function (BRDF) model and instantaneous albedo. For snow-covered surfaces, we provide subpixel snow fraction and snow grain size. All products come in standard HDF4 (software library) format at 1 km resolution, except for BRF, which is also provided at 500 m resolution on a sinusoidal grid adopted by the MODIS Land team. All products are provided on per-observation basis in daily files except for the BRDF/Albedo product, which is reported every 8 days. Because MAIAC uses a time series approach, BRDF/Albedo is naturally gap-filled over land where missing values are filled-in with results from the previous retrieval. While the BRDF model is reported for MODIS Land bands 1-7 and ocean band 8, BRF is reported for both land and ocean bands 1-12. This paper focuses on MAIAC cloud detection, aerosol retrievals and atmospheric correction and describes MCD19 data products and quality assurance (QA) flags.

MAIAC Algorithm↗
NASA NTRS2018

Evaluation of the Danish Aerospace Corporation Portable Pulmonary Function System

A research project designed to investigate changes in maximal oxygen consumption (VO2max) during and following long duration flight on the International Space Station (ISS) has recently been completed. The device used to measure VO2 on board ISS, the Portable Pulmonary Function System (PPFS) manufactured by the Danish Aerospace Corporation (DAC), is based on previous-generation devices manufactured by DAC, but the PPFS has not been validated for analyzing metabolic gases or measuring cardiac output (Qc). The purpose of the present evaluation is to compare PPFS metabolic gas analysis measurements to measurements obtained using a clinically-validated system (ParvoMedics TrueOne(c) 2400 system; Parvo). In addition, Qc data collected with the PPFS were compared to Qc measurements from echocardiography. METHODS: Ten subjects completed three cycle exercise tests to maximal exertion. The first test was conducted to determine each subject's VO2max and set the work rates for the second and third (comparison) tests. The protocol for the two comparison tests consisted of three 5-minute stages designed to elicit 25%, 50%, and 75% VO2max (based upon results from the initial test), followed by 1-minute stages of increasing work rate (25 watt/minute) until the subject reached maximal effort. During one of the two comparison tests, metabolic gases and Qc were assessed with the PPFS; metabolic gases and Qc were assessed with the Parvo and by echocardiography, respectively, during the other test. The order of the comparison tests was counterbalanced. VO2max and maximal work rate during the comparison tests were compared using t tests. Mixed-effects regression modeling was used to analyze submaximal data. RESULTS: All of the data were within normal physiological ranges. The PPFS-measured values for VO2max were 6% lower than values obtained with the Parvo (PPFS: 3.11 +/- 0.75 L/min; Parvo: 3.32 +/- 0.87 L/min; mean +/- standard deviation; P = 0.02); this difference is probably due to flow restriction imposed by the PPFS Qc accessories. Submaximal VO2 values were slightly lower when measured with the PPFS, although differences were not physiologically relevant. The PPFS-measured values of submaximal carbon dioxide production (VCO2) were lower than the data obtained from Parvo, which could be attributed to lower fractions of expired carbon dioxide measured by the PPFS. The PPFS Qc values tended to be lower than echocardiography-derived values. CONCLUSIONS: The results of the present study indicate a need to further examine the PPFS and to better quantify its reproducibility; however, none of the findings of the current evaluation indicate that the PPFS needs to be replaced or modified.

Portable Pulmonary Function System (PPFS)↗
DOE OSTI2022

PyOECP: A flexible open-source software library for estimating and modeling the complex permittivity based on the open-ended coaxial probe (OECP) technique

Here, we present PyOECP, a Python-based flexible open-source software for estimating and modeling the complex permittivity obtained from the open-ended coaxial probe (OECP) technique. The transformation of the measured reflection coefficient to complex permittivity is performed based on three different methods. The software library contains the dielectric spectra of common reference liquids, which can be used to transform the reflection coefficient into the dielectric spectra. Several Python routines that are commonly employed (e.g., SciPy and NumPy) in the field of science and engineering are required only so that the users can alter the software structure depending on their needs. The modeling algorithm exploits the Markov Chain Monte Carlo method for the data regression. The discrete relaxation models can be built by a proper combination of well-known relaxation models. In addition to these models, electrode polarization, a typical measurement artifact for interpreting dielectric spectra, can be incorporated into the modeling algorithm. A continuous relaxation model, which solves the Fredholm integral equation of the first kind (a mathematically ill-posed problem), is also included. This open-source software enables users to freely adjust the physical parameters to obtain physical insight into their materials under test and will be consistently updated for more accurate measurement and interpretation of dielectric spectra in an automated manner. This work describes the theoretical and mathematical background of the software, lays out the workflow, and validates the software functionality based on both synthetic and empirical data included in the software.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Machine learning predictions of high-Curie-temperature materials

Technologies that function at room temperature often require magnets with a high Curie temperature, $T$ C , and can be improved with better materials. Discovering magnetic materials with a substantial $T$ C is challenging because of the large number of candidates and the cost of fabricating and testing them. Using the two largest known datasets of experimental Curie temperatures, we develop machine-learning models to make rapid $T$ C predictions solely based on the chemical composition of a material. We train a random-forest model and a k -NN one and predict on an initial dataset of over 2500 materials and then validate the model on a new dataset containing over 3000 entries. The accuracy is compared for multiple compounds' representations (“descriptors”) and regression approaches. A random-forest model provides the most accurate predictions and is not improved by dimensionality reduction or by using more complex descriptors based on atomic properties. Further, a random-forest model trained on a combination of both datasets shows that cobalt-rich and iron-rich materials have the highest Curie temperatures for all binary and ternary compounds. An analysis of the model reveals systematic error that causes the model to over-predict low-$T$ C materials and under-predict high-$T$ C materials. For exhaustive searches to find new high-$T$ C materials, analysis of the learning rate suggests either that much more data is needed or that more efficient descriptors are necessary.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Loss of P53 regresses cardiac remodeling induced by pressure overload partially through inhibiting HIF1α signaling in mice

The tumor suppressor p53 is recognized as the guardian of the genome in cell cycle and cell death. P53 expression increases as cardiac hypertrophy worsens to heart failure, suggesting that p53 may play important role in cardiac remodeling. In the present study, deletion of p53 in the mice heart would ameliorate cardiac hypertrophy induced by pressure overload. The role of p53 on heart was investigated using in vivo models. Cardiac hypertrophy in mice was induced by transverse aortic banding surgery. The extent of cardiac hypertrophy was examined by echocardiography, as well as pathological and molecular analyses of heart tissue. Global knockout of p53 in the mice reduced the hypertrophic response and markedly reduced cardiac apoptosis, and fibrosis. Ejection fraction of heart was also improved in hearts without p53 in response to pressure overload. Protein determination further suggested loss of p53 expression markedly increased Hypoxia-inducible factor 1-alpha (HIF1α) and vascular endothelial growth factor (VEGF) expression. The study indicated p53 deteriorated cardiac functions and cardiac hypertrophy, apoptosis, and fibrosis by partially inhibition of HIF1α and VEGF.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2007

Aeroelastic Model Structure Computation for Envelope Expansion

Structure detection is a procedure for selecting a subset of candidate terms, from a full model description, that best describes the observed output. This is a necessary procedure to compute an efficient system description which may afford greater insight into the functionality of the system or a simpler controller design. Structure computation as a tool for black-box modelling may be of critical importance in the development of robust, parsimonious models for the flight-test community. Moreover, this approach may lead to efficient strategies for rapid envelope expansion which may save significant development time and costs. In this study, a least absolute shrinkage and selection operator (LASSO) technique is investigated for computing efficient model descriptions of nonlinear aeroelastic systems. The LASSO minimises the residual sum of squares by the addition of an l(sub 1) penalty term on the parameter vector of the traditional 2 minimisation problem. Its use for structure detection is a natural extension of this constrained minimisation approach to pseudolinear regression problems which produces some model parameters that are exactly zero and, therefore, yields a parsimonious system description. Applicability of this technique for model structure computation for the F/A-18 Active Aeroelastic Wing using flight test data is shown for several flight conditions (Mach numbers) by identifying a parsimonious system description with a high percent fit for cross-validated data.

Kukreja, Sunil L.↗
DOE OSTI2020

Machine learning-based prediction of enzyme substrate scope: Application to bacterial nitrilases

Predicting the range of substrates accepted by an enzyme from its amino acid sequence is challenging. Although sequenc- and structure-based annotation approaches are often accurate for predicting broad categories of substrate specificity, they generally cannot predict which specific molecules will be accepted as substrates for a given enzyme, particularly within a class of closely related molecules. Combining targeted experimental activity data with structural modeling, ligand docking, and physicochemical properties of proteins and ligands with various machine learning models provides complementary information that can lead to accurate predictions of substrate scope for related enzymes. Here we describe such an approach that can predict the substrate scope of bacterial nitrilases, which catalyze the hydrolysis of nitrile compounds to the corresponding carboxylic acids and ammonia. Each of the four machine learning models (logistic regression, random forest, gradient-boosted decision trees, and support vector machines) performed similarly (average ROC = 0.9, average accuracy = ~82%) for predicting substrate scope for this dataset, although random forest offers some advantages. Finally, this approach is intended to be highly modular with respect to physicochemical property calculations and software used for structural modeling and docking.

59 BASIC BIOLOGICAL SCIENCES↗